What Makes a Song Award-Winning?
Cristian Granchelli, Joan Orellana Rios, Cameron Kelahan
2023-08-25
Can we use attributes and metrics of songs to predict their likelihood of winning a Grammy?
What common traits, if any, do award winning songs contain? Can we look at intrinsic traits of songs, combined with metrics defined by Spotify, to determine award winning musical features? In this paper, we break down our data collection, data processing, and data analysis of a dataset of roughly 1,000 popular songs, both award-winning and not.
The Data
Data Collection and Selection
JOAN TO DO: Write how we chose the 1,000 songs originally; how we ended up with 867; and which Grammy winners we stuck with (only Grammy winners, or also Grammy nominees?)
# Set the working directory to this file's folder
library("rstudioapi")
setwd(dirname(getActiveDocumentContext()$path))
load("final_df_n_str.RData")
Sys.setenv(LANG = "en")
# Load necessary libraries
library(pROC)
library(MASS)
library(ROSE)
library(confintr)
library(ggplot2)
library(correlation)
library(corrplot)
library(class)
library(caret)
library(glmnet)# Selecting the relevant variables
data = final_df_n_str
data = data[,c("track_name", "artist_name", "IsWinner", "Year","year",
"followers", "acousticness", "danceability", "duration_ms",
"energy", "instrumentalness", "key", "liveness", "loudness",
"mode", "tempo", "time_signature", "valence")]
# Merge the two year variable
data$Year[data$Year == "Undefined"] <- data$year[data$Year == "Undefined"]
data = data[,c("track_name","artist_name", "IsWinner", "Year", "followers",
"acousticness", "danceability", "duration_ms",
"energy", "instrumentalness", "key", "liveness", "loudness",
"mode", "tempo", "time_signature", "valence")]
# Eliminating duplicates
data$track_name == "Closing Time"
data$track_name == "Smells Like Teen Spirit"
data$track_name == "Don't Wanna Fight"
data[914, ]
data[789,]
data[669,]
data = data[-c(669, 789, 914),]
sum(data$Year < 1992)
nrow(data)
data = data[!data$Year < 1992,]
# Creating row names
names = paste0(data$track_name, " - ", data$artist_name)
# Eliminating unusable variables
data = data[,c("IsWinner", "Year", "followers", "acousticness",
"danceability", "duration_ms", "energy",
"instrumentalness", "key", "liveness", "loudness", "mode",
"tempo", "time_signature", "valence")]
data = cbind(names = names, data)
# Casting variables
data$IsWinner[data$IsWinner == "Winner"] = 1
data$IsWinner[data$IsWinner == "Nominee"] = 1
data$IsWinner[data$IsWinner == "Nothing"] = 0
data$IsWinner = as.integer(data$IsWinner)
data$Year = as.integer(data$Year)
data$mode = as.factor(data$mode)
data$key = as.factor(data$key)
data$time_signature = as.factor(data$time_signature)
# Giving row names
summary(data)
summary(data$IsWinner)Explanation of Variables
In order to perform analysis of songs, we decided to use metrics that are intrinsic to music as well as artificial metrics created and measured by the music streaming giant Spotify. The intrinsic metrics we used were: duration, musical key, modality (major or minor key), tempo, time signature, and genre. Spotify also uses what they call “audio features” (in the table below) to perform their own analysis of songs when creating playlists, suggesting music, etc. We used these professionally manufactured metrics to bolster the intrinsic metrics and increase our insight into what might make a song award-winning.
| Audio Feature | Definition |
|---|---|
| Acousticness | A confidence measure from 0.0 to 1.0 of whether the track is acoustic. 1.0 represents high confidence the track is acoustic. |
| Danceability | Danceability describes how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity. A value of 0.0 is least danceable and 1.0 is most danceable. |
| Energy | Energy is a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity. Typically, energetic tracks feel fast, loud, and noisy. For example, death metal has high energy, while a Bach prelude scores low on the scale. Perceptual features contributing to this attribute include dynamic range, perceived loudness, timbre, onset rate, and general entropy. |
| Instrumentalness | Predicts whether a track contains no vocals. “Ooh” and “aah” sounds are treated as instrumental in this context. Rap or spoken word tracks are clearly “vocal”. The closer the instrumentalness value is to 1.0, the greater likelihood the track contains no vocal content. Values above 0.5 are intended to represent instrumental tracks, but confidence is higher as the value approaches 1.0. |
| Liveness | Detects the presence of an audience in the recording. Higher liveness values represent an increased probability that the track was performed live. A value above 0.8 provides strong likelihood that the track is live. |
| Loudness | The overall loudness of a track in decibels (dB). Loudness values are averaged across the entire track and are useful for comparing relative loudness of tracks. Loudness is the quality of a sound that is the primary psychological correlate of physical strength (amplitude). Values typically range between -60 and 0 db. |
| Speechiness | Speechiness detects the presence of spoken words in a track. The more exclusively speech-like the recording (e.g. talk show, audio book, poetry), the closer to 1.0 the attribute value. Values above 0.66 describe tracks that are probably made entirely of spoken words. Values between 0.33 and 0.66 describe tracks that may contain both music and speech, either in sections or layered, including such cases as rap music. Values below 0.33 most likely represent music and other non-speech-like tracks. |
| Valence | A measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track. Tracks with high valence sound more positive (e.g. happy, cheerful, euphoric), while tracks with low valence sound more negative (e.g. sad, depressed, angry). |
Although Spotify does not openly share how they determine these metrics, we found them suitable to assist in our analysis.
As a final processing step, we split the data into training and test datasets. The training dataset contains 80% of the original dataset, and the remaining 20% of the data in the test dataset will be used to test tour model against after we have trained it. It is very important to test the model on never-before-seen data to determine not only how well the model performs, but also how well the model can generalize.
# Splitting training and test set
training_size = floor(0.8 * nrow(data))
set.seed(42)
train_ind = sample(seq_len(nrow(data)), size = training_size)
training_set = data[train_ind,]
test_set = data[-train_ind,]
summary(training_set)## names IsWinner Year followers
## Length:693 Min. :0.0000 Min. :1992 Min. : 2597
## Class :character 1st Qu.:0.0000 1st Qu.:2001 1st Qu.: 868777
## Mode :character Median :0.0000 Median :2010 Median : 2350118
## Mean :0.1876 Mean :2009 Mean : 4338356
## 3rd Qu.:0.0000 3rd Qu.:2018 3rd Qu.: 5615666
## Max. :1.0000 Max. :2023 Max. :44692754
##
## acousticness danceability duration_ms energy
## Min. :0.0000032 Min. :0.130 Min. : 78591 Min. :0.0975
## 1st Qu.:0.0016900 1st Qu.:0.419 1st Qu.: 206413 1st Qu.:0.6040
## Median :0.0278000 Median :0.522 Median : 237800 Median :0.7570
## Mean :0.1553733 Mean :0.512 Mean : 251635 Mean :0.7182
## 3rd Qu.:0.2050000 3rd Qu.:0.607 3rd Qu.: 278267 3rd Qu.:0.8820
## Max. :0.9880000 Max. :0.894 Max. :1355938 Max. :0.9960
##
## instrumentalness key liveness loudness mode
## Min. :0.00e+00 9 : 95 Min. :0.0157 Min. :-18.148 0:203
## 1st Qu.:4.90e-06 2 : 94 1st Qu.:0.0989 1st Qu.: -8.086 1:490
## Median :3.21e-04 7 : 84 Median :0.1240 Median : -6.253
## Mean :6.25e-02 0 : 81 Mean :0.2004 Mean : -6.645
## 3rd Qu.:1.49e-02 11 : 68 3rd Qu.:0.2320 3rd Qu.: -4.767
## Max. :8.95e-01 4 : 58 Max. :0.9980 Max. : -1.574
## (Other):213
## tempo time_signature valence
## Min. : 48.58 1: 2 Min. :0.0494
## 1st Qu.: 99.19 3: 37 1st Qu.:0.3050
## Median :121.14 4:649 Median :0.4640
## Mean :123.28 5: 5 Mean :0.4725
## 3rd Qu.:141.93 3rd Qu.:0.6310
## Max. :205.85 Max. :0.9730
## # Checking if the ratio is preserved
sum(data$IsWinner == 1)/ sum(data$IsWinner == 0)## [1] 0.2159888sum(training_set$IsWinner == 1)/ sum(training_set$IsWinner == 0)## [1] 0.2309059training_set## # A tibble: 693 × 16
## names IsWinner Year followers acousticness danceability duration_ms energy
## <chr> <int> <int> <int> <dbl> <dbl> <int> <dbl>
## 1 Nightm… 0 2010 6262809 0.000318 0.554 374453 0.949
## 2 I'd Do… 0 1993 1034322 0.465 0.366 718600 0.561
## 3 Patien… 1 2022 4802169 0.000195 0.318 441402 0.87
## 4 Someda… 1 2006 6137375 0.254 0.533 295560 0.59
## 5 I Know… 0 2020 1775452 0.33 0.323 344693 0.323
## 6 Find M… 1 2021 4416749 0.256 0.873 293849 0.809
## 7 Weak -… 0 2017 2923531 0.118 0.67 201159 0.643
## 8 Walk O… 1 2001 11148674 0.00379 0.528 296240 0.832
## 9 Black … 1 2018 2341237 0.197 0.558 259893 0.902
## 10 Spectr… 0 2012 6399322 0.00225 0.578 218190 0.946
## # ℹ 683 more rows
## # ℹ 8 more variables: instrumentalness <dbl>, key <fct>, liveness <dbl>,
## # loudness <dbl>, mode <fct>, tempo <dbl>, time_signature <fct>,
## # valence <dbl>Exploratory Data Analysis
Relationship Between Independent Variables
At first, we took a look at the continuous variables.
attach(training_set)## The following object is masked _by_ .GlobalEnv:
##
## names# Correlations between continuous variables
cor_matrix = cor(training_set[,c(-1, -2, -10, -13, -15)])
corrplot(cor_matrix)
pairs(training_set[,c(-1, -2, -10, -13, -15)], lower.panel = panel.smooth)
WHY CAN’T I GET THIS PAIRS() PDF TO INSERT??
knitr::include_graphics("yourPlot.pdf", error = FALSE)When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:
summary(cars)## speed dist
## Min. : 4.0 Min. : 2.00
## 1st Qu.:12.0 1st Qu.: 26.00
## Median :15.0 Median : 36.00
## Mean :15.4 Mean : 42.98
## 3rd Qu.:19.0 3rd Qu.: 56.00
## Max. :25.0 Max. :120.00Including Plots
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